Person-level disambiguation for PubMed authors and NIH grant applicants
Description
A person or entity may use several name variations on publications and/or grants, which causes uncertainty in attributing research contributions
Detailed example
Harmonized data
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Correct attribution of grants, articles, and other products to individual researchers is critical for high quality person-level analysis. This improved method for disambiguation of authors on articles in PubMed and NIH grant applicants can inform data-driven decision making
Controls / human review
ATO: No; PIA: Not published
Data needed
Biomedical publications and preprints from PubMed and select publicly available preprint servers, grants titles, abstracts and biosketches from IMPACII, and ORCID data